Back

Upstream translation initiation expands the coding capacity of segmented negative-strand RNA viruses

Sloan, E.; Alenquer, M.; Chung, L.; Clohisey, S. M. R.; Dinan, A. M.; Gifford, R. J.; Gu, Q.; Irigoyen, N.; Jones, J. D.; van Knippenberg, I.; Rezelj, V. V.; Wang, B.; Wise, H.; Amorim, M.-J.; Baillie, J. K.; Brierley, I.; Digard, P.; Firth, A.; MacLeod, M. K.; Hutchinson, E.

2019-10-08 molecular biology
10.1101/795815 bioRxiv
Show abstract

Segmented negative-strand RNA viruses (sNSVs) include the influenza viruses, the bunyaviruses, and other major pathogens of humans, other animals and plants. The genomes of these viruses are extremely short. In response to this severe genetic constraint, sNSVs use a variety of strategies to maximise their coding potential. Because the eukaryotic hosts parasitized by sNSVs can regulate gene expression through low levels of translation initiation upstream of their canonical open reading frames (ORFs), we asked whether sNSVs could use upstream translation initiation to expand their own genetic repertoires. Consistent with this hypothesis, we showed that influenza A viruses (IAVs) and bunyaviruses were capable of upstream translation initiation. Using a combination of reporter assays and viral infections, we found that upstream translation in IAVs can initiate in two unusual ways: through non-AUG initiation in virally encoded untranslated regions, and through the appropriation of an AUG-containing leader sequence from host mRNAs through viral cap-snatching, a process we termed start-snatching. Finally, while upstream translation of cellular genes is mainly regulatory, for sNSVs it also has the potential to create novel viral gene products. If in frame with a viral ORF, this creates N-extensions of canonical viral proteins. If not, it allows the expression of cryptic overlapping ORFs, which we found were highly conserved in IAV and widely distributed in peribunyaviruses. Thus, by exploiting their hosts capacity for upstream translation initiation, sNSVs can expand still further the coding potential of their extremely compact RNA genomes.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.